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From "Sean Owen (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-2401) AdaBoost.MH, a multi-class multi-label classifier
Date Tue, 25 Nov 2014 14:15:12 GMT

    [ https://issues.apache.org/jira/browse/SPARK-2401?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14224575#comment-14224575
] 

Sean Owen commented on SPARK-2401:
----------------------------------

This looks like a duplicate of SPARK-1546. At least the first effort should be to get any
AdaBoost algo at all going.

> AdaBoost.MH, a multi-class multi-label classifier
> -------------------------------------------------
>
>                 Key: SPARK-2401
>                 URL: https://issues.apache.org/jira/browse/SPARK-2401
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>            Reporter: Gang Bai
>            Priority: Trivial
>
> Multi-class multi-label classifiers are very useful in web page profiling, audience segmentation
etc. The goal of a multi-class multi-label classifier is to tag a sample data point with a
subset of labels from a finite, pre-specified set, e.g. tagging a visitor with a set of interests.
Given a set of L labels, a data point can be tagged with one of the 2^L possible subsets.
The main challenges in training a multi-class multi-label classifier are the exponentially
large label space. 
> This JIRA is created to track the effort of solving the training problem of multi-class,
multi-label classifiers by implementing AdaBoost.MH on Apache Spark. It will not be an easy
task. I will start from a basic DecisionStump weak learner and a simple Hamming tree resembling
DecisionStumps into a meta weak learner, and the iterative boosting procedure. I will be reusing
modules of Alexander Ulanov's multi-class and multi-label metrics evaluation and Manish Amde's
decision tree/boosting/ensemble implementations. 



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